A search for new symbiotic stars in the Milky Way: Using machine learning techniques applied to photometric databases

TL;DR

This paper develops a machine learning method using photometric data to identify new symbiotic stars in the Milky Way, significantly expanding the known population.

Contribution

It introduces a Random Forest model trained on confirmed systems and applies it to millions of sources to discover new candidates with high confidence.

Findings

Achieved an 89% F1-score in classifying symbiotic stars.

Identified 990 candidates with >70% probability, refined to 12 high-confidence objects.

Validated the method by recovering 92.3% of recently confirmed systems.

Abstract

Symbiotic stars (SySts) are interacting binaries composed of a red giant transferring material to a hot compact star, typically a white dwarf. Although only about 300 systems are confirmed, the Galactic population is estimated at 1.2 x 10^3 - 1.5 x 10^4, indicating that most remain undiscovered. We identify new SySts using a machine-learning approach that combines Gaia DR3, 2MASS, and WISE photometry, parallaxes, and the pseudo-equivalent width of H alpha. A Random Forest model was trained on 166 confirmed S-type SySts and 1600 non-symbiotic stars, applying SMOTE to mitigate class imbalance. The model achieved an F1-score of 89% for the symbiotic class. Applied to 2.5 x 10^6 color-selected sources, it identified 990 candidates with probabilities more than 70%. We further refined the sample using physically motivated cuts on effective temperature, surface gravity, metallicity, and SkyMapper photometry, yielding 12 high-confidence candidates. These objects show cool temperatures, low surface gravities, near-solar metallicity, H alpha emission, moderate-to-high luminosities, and UV excess consistent with S-type SySts. Validation on recently confirmed systems recovered 92.3%, demonstrating the robustness and generalizability of our method.